We compare methods to measure comovement in business cycle data using multi-level dynamic factor models. To do so, we employ a Monte Carlo procedure to evaluate model performance for different specifications of factor models across three different estimation procedures. We consider three general factor model specifications used in applied work. The first is a single-factor model, the second a two-level factor model, and the third a three-level factor model. Our estimation procedures are the Bayesian approach of Otrok and Whiteman (1998), the Bayesian state-space approach of Kim and Nelson (1998) and a frequentist principal components approach. The latter serves as a benchmark to measure any potential gains from the more computationally intensive Bayesian procedures. We then apply the three methods to a novel new dataset on house prices in advanced and emerging markets from Cesa-Bianchi, Cespedes, and Rebucci (2015) and interpret the empirical results in light of the Monte Carlo results.
Diana A. Cooke and Hannah G. Shell provided research assistance. We thank two referees and Siem Jan Koopman for helpful comments. Matlab code used in this paper is available at www.runmycode.org. The views expressed herein do not reflect the views of the Federal Reserve Bank of St. Louis, the Federal Reserve System or the World Bank.
Jackson, L.E., Kose, M.A., Otrok, C. and Owyang, M.T. (2016), "Specification and Estimation of Bayesian Dynamic Factor Models: A Monte Carlo Analysis with an Application to Global House Price Comovement", Dynamic Factor Models (Advances in Econometrics, Vol. 35), Emerald Group Publishing Limited, Bingley, pp. 361-400. https://doi.org/10.1108/S0731-905320150000035009
Emerald Group Publishing Limited
Copyright © 2016 Emerald Group Publishing Limited